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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 107-114     DOI: 10.6046/zrzyyg.2021460
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An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+
ZHAO Linghu1(), YUAN Xiping2,3, GAN Shu1,2(), HU Lin1, QIU Mingyu1
1. School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China
3. School of Earth Science and Engineering, West Yunnan University of Applied Sciences, Dali 671000, China
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Abstract  

Aiming at the problems of poor extraction effect and slow extraction speed of traditional road extraction methods in the information extraction of roads from high-resolution remote sensing images, this study proposed a new information extraction model based on improved Deeplabv3+. In the new model, the combination of the MobileNetv2 backbone feature extraction network with the Dice Loss function effectively balanced the contradiction between the precision and speed of road information extraction from high-resolution remote sensing images. As a result, high extraction precision was achieved while meeting timeliness requirements by reducing model parameters. The experimental results based on the open-source road information extraction dataset show that: ① The road information extraction model proposed in this study was feasible for high-resolution remote sensing images, with overall accuracy of up to 98.71%; ② In terms of the information extraction speed, the new model had an average frame number of 120.05 and parameter amount of only 5.81 M. Therefore, the new model was more lightweight lighter than original models, meeting the timeliness requirements. Therefore, the model proposed in this study meets the timeliness requirements by greatly reducing the parameter amount while ensuring high extraction accuracy. This study provides a new philosophy and method for improving the accuracy and speed of road information extraction from high-resolution images.
Keywords remote sensing image      road information extraction      deep learning      semantic segmentation      Deeplabv3+ model     
ZTFLH:  P2  
Issue Date: 20 March 2023
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Linghu ZHAO
Xiping YUAN
Shu GAN
Lin HU
Mingyu QIU
Cite this article:   
Linghu ZHAO,Xiping YUAN,Shu GAN, et al. An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+[J]. Remote Sensing for Natural Resources, 2023, 35(1): 107-114.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021460     OR     https://www.gtzyyg.com/EN/Y2023/V35/I1/107
Fig.1  Flow chart of this study
Fig.2  Structure of Deeplabv3+
Fig.3  Structure of DepSep Conv
输入/(像素×
通道数)		 模块类型 t c n s
2242×3 Conv2d - 32 1 2
1122×32 Bottleneck 1 16 1 1
1122×16 Bottleneck 6 24 2 2
562×24 Bottleneck 6 32 3 2
282×32 Bottleneck 6 64 4 2
142×64 Bottleneck 6 96 3 1
142×96 Bottleneck 6 160 3 2
72×160 Bottleneck 6 320 1 1
72×320 Conv2d 1×1 - 1 280 1 1
72×1 280 Avgpool 7×7 - - 1 -
1×1×1 280 Conv2d 1×1 - k - -
Tab.1  Structure of MobileNetv2
Fig.4  Bottleneck layer
Fig.5  Comparison of ordinary convolution and atrous convolution
序号 原始图像 标签图 提取结果
1
2
3
Tab.2  Extraction results of the road by using improved the Deeplabv3+ model
模型 OA/% P/% R/% F1/% 参数
量/M		 平均
帧数
PSPNet 96.59 63.59 77.16 69.72 2.38 129.56
U-Net 98.26 82.46 82.33 83.39 24.89 49.71
Deeplabv3+ 98.45 87.00 81.92 84.38 54.71 30.77
改进Deeplabv3+ 98.71 87.49 87.06 87.27 5.81 120.05
Tab.3  Comparing the results for the different models
模型 区域a 区域b 区域c 区域d
遥感影像
PSPNet
U-Net
Deeplabv3+
改进Deeplabv3+
真实道路
Tab.4  Comparison of the results of four road extraction models
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